CN102499676A - Effective time sequence and electrode recombination based electroencephalograph signal categorizing system and method - Google Patents
Effective time sequence and electrode recombination based electroencephalograph signal categorizing system and method Download PDFInfo
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Abstract
The invention discloses an effective time sequence and electrode recombination based electroencephalograph signal categorizing system and a method, which can realize the identification to three types of character expression (happiness, normality and sadness) by acquiring and analyzing the electroencephalograph signals of the human brain, and mainly comprises an electroencephalograph signal acquisition process and an electroencephalograph signal analyzing process. According to the invention, the electroencephalograph signals are acquired by stimulating different expression of a testee; the feature space of the effective electroencephalograph signals is determined by the strong energy distribution of the full field of the electroencephalograph signals; then, the PCA (Principal Component Analysis) dimension reduction is carried out to the original electroencephalograph signals corresponding to the feature space, and the electroencephalograph signals with categorizing advantages are reconstructed; and finally, a linear discriminant function categorizer is selected to for categorizing. According to the invention, during expression identification, only target features are extracted from the acquired electroencephalograph signals, then, the acquired electroencephalograph signals are categorized, and the identification result can be determined; and the identification of the electroencephalograph signals can be realized based on the character expression stimulation. In the invention, the cognition of the human being is introduced, and the advantages of objectiveness and high efficiency are provided.
Description
Technical field
The present invention relates to a kind of processing and analytical method of EEG signals, particularly relate to a kind of eeg signal classification system and method based on effective time sequence and electrode reorganization.
Background technology
Social day by day closely at current this inter personal contact, the expression of correctly discerning other people has important significance of existence.This not only can make people in time regulate factum and conform, and can also avoid unnecessary danger effectively, helps social communication and environmental adaptation.Simultaneously, normal person's research also can be clinical diagnosis and treatment provides reference, be used for prevention and treatment work.At present, the main application of human face expression recognition technology comprises man-machine interaction, safety, robot manufacturing, medical treatment, communication and automotive field etc.
In the document of relevant Expression Recognition, mainly judge expression, but the traditional method of these expression assessments has subjectivity, is easy to denied by other people through image Expression Recognition and speech signal analysis.Yet another kind of available Expression Recognition way is the physiology electroencephalogramsignal signal analyzing, and it is Expression Recognition means more directly perceived, effective, because the expression state was reflected by neural activity originally.
Summary of the invention
The objective of the invention is to stimulates the EEG signals that produce to expression, proposes a kind of eeg signal classification system and method based on effective time sequence and electrode reorganization.To avoid the subjectivity of characteristic, carry out efficient through adopting the parallel computation strategy to improve.
The present invention adopts following technological means to realize:
A kind of eeg signal classification system based on effective time sequence and electrode reorganization, comprising: eeg signal acquisition module, EEG signals pre-processing module, EEG signals feature selection module, EEG signals expression classification are implemented module.
The EEG signals information acquisition module is gathered by the original EEG signals of examination under glad, neutral and sad different expressions stimulate, and the EEG signals that collect are passed to the EEG signals pre-processing module; The original EEG signals that the EEG signals pre-processing module will collect carry out denoising (noise comprises level eye electricity and vertical eye), convert purified EEG signals to overall field intensity afterwards and send into brain electrical feature selection module; The EEG signals feature selection module; Confirm the effective time zone of EEG signals through the peak value characteristic of overall field intensity EEG signals; EEG signals on the effective time zone are being carried out the electrode reorganization; With the EEG signals dimensionality reduction backsight after the reorganization is the final characteristic of EEG signals Expression Recognition, and this characteristic is sent to EEG signals expression classification enforcement module; The EEG signals expression classification is implemented module and is carried out eeg signal classification with classical sorting algorithm (Fisher grader).
A kind of eeg signal classification method based on effective time sequence and electrode reorganization may further comprise the steps:
Step 1, the experimenter is with the polar cap that powers on, original EEG signals be through 66 lead 10/20 method that international electroencephalography can demarcate the EEG amplifier gather, and choose all electrode positions, gather experimenter's EEG signals of different expression stimulating courses;
Step 2 is input to pre-processing module with the EEG signals that collect, and pre-processing module is mainly carried out denoising to the EEG signals that collect, and it is strong to obtain the whole audience of EEG signals, and the whole audience of EEG signals is the superposed average value of all electrode signals by force;
Step 3 through the characteristic extracting module analysis strong to the EEG signals whole audience, is confirmed the effective time zone of EEG signals feature selection;
Step 4 because the EEG signals that different electrodes produce have different physiologic meanings, is therefore carried out the electrode reorganization through characteristic extracting module to the EEG signals of feature selection;
Step 5 in order to reduce the redundancy of EEG signals, is carried out dimensionality reduction to the EEG signals after the resulting reorganization of step 4 through principal component analysis (PCA) method;
Step 6, the linear discriminant function grader (Fisher) that uses the EEG signals expression classification to implement in the module to the EEG signals after the characteristic extracting module extraction carries out classification learning and test;
During the test Expression Recognition; Through the eeg signal acquisition module, gather the EEG signals that tried to be measured, EEG signals are sent into the EEG signals pre-processing module; After removing noise; Calculate to generate according to the EEG feature extraction module again and tried the characteristic of correspondence vector, then this characteristic vector is sent into the EEG signals expression classification and implement module, obtain the eeg signal classification result who expresses one's feelings and stimulate at last.
The selection in the effective time zone of EEG signals, regional according to the effective time that the peak value and the high-energy value of overall field intensity are confirmed; The process of electrode reorganization is on the basis in effective time zone, the process that different electrodes are reconfigured; The process of EEG signals expression classification is on parallel basis, and the EEG signals characteristic of selecting is classified.
A kind of eeg signal classification system and method based on effective time sequence and electrode reorganization of the present invention compared with prior art has the following advantages:
1, compare with traditional method, the present invention utilizes the physiology EEG signals, has avoided the subjectivity of characteristic.
2, in step (3), to carry out feature selection by force according to the whole audience of EEG signals be a kind of reasonable and effective new method in the present invention.
3, the present invention's employed principal component analytical method in step (5) is the classical way in the statistical learning, in many numerical computations platforms, can find the implementation algorithm of comparative maturity.
4, main amount of calculation of the present invention concentrates on step (6); Owing to can produce multiple combination of electrodes in step (4); Therefore step (6) will be carried out the grader training to the brain electrical feature under every kind of combination and estimated, and therefore can adopt the parallel computation strategy to improve execution efficient.
Description of drawings
Fig. 1 is the flow chart and the system module dividing condition of method overall process involved in the present invention;
Fig. 2 is the experimental design flow chart of collection EEG signals involved in the present invention;
Fig. 3 is involved in the present invention based on the strong EEG signals figure of the whole audience;
Fig. 4 is the idiographic flow of " EEG Processing " part among Fig. 1 of the present invention.
The specific embodiment
Below in conjunction with the specific embodiment the present invention is done further explanation.
The step of the present invention when training Expression Recognition grader has following 6 steps:
At first in step 1, carry out the collection of EEG signals according to the experiment that designs; In the process that test is gathered; Select for use three types of human face expressions as stimulating picture, comprise glad expression, neutral expression and sad expression, each expression has 18 kinds of shapes of face; Each subjects carries out 408 tests, and three generic tasks respectively account for 136 times.Each process of the test is following: at first give subjects's display reminding language; After treating that the subjects presses space bar; Show a forward or swing to that expressing one's feelings is the picture of one of glad, neutral, sad three kinds of expressions, treat the subjects to expression know distinguish and press corresponding button after; The expression single test finishes, and detailed process is as shown in Figure 2.By the examination data acquisition from 12 ages the healthy subjects in 20-30 year.
Next the original EEG signals that step 1 collected carry out pretreatment, and pre-processing module comprises 2 steps:
Step 2.1 very easily receives the influence of electro-ocular signal because EEG signals are faint.Therefore, the noise of removing in the EEG signals just seems particularly important, utilizes NeuroScan software that the EEG signals that collect are carried out denoising in the inventive method.
Step 2.2 obtains in step 2.1 on the basis of EEG signals EEG of cleaning, and it is strong to obtain the corresponding whole audience of the original EEG signals of 66 conductive electrode through NeuroScan software, i.e. GFP, and it is to carry out superposed average through the signal to each electrode to obtain.
Next step 3 compares analysis according to characteristic extracting module to the whole audience of three types of original EEG signals strong (GFP), and as shown in Figure 3, the result finds the strong peak Distribution of the whole audience at 88ms, 154ms, 232ms place, and to be slow potential reflect at the 350ms-650ms place.Therefore, we distributed according to the time of strong (GFP) energy of the whole audience, and (200ms-250ms 350ms-650ms) confirms the effective time section (being that effective time is regional) of original EEG signals (EEG) feature selection for 70-110ms, 125ms-185ms.
In the step 4, we adopt the heuristics method in the characteristic extracting module, and heuristic function is the performance of linear discriminant function grader, and promptly we analyze the effectiveness of reorganization with the performance quality.At first through the electrode reorganization EEG signals feature selection is done further extraction, promptly recombinate in the determined validity feature of step 3 zone, the process of reorganization is to adopt exhaustive mode to carry out permutation and combination according to the different time zone with different electrodes.Formula is following:
i∈{1，…，64}，j∈{1，…，5}，
Wherein, i is a number of poles, and j is a time series section number, E
IjRefer to the EEG signals of j time period of i electrode, work as α
i, represent that i unipolar EEG signals are used as characteristic, work as β at=1 o'clock
j, represent that the EEG signals of j time zone are used as characteristic at=1 o'clock.
Next various combination of electrodes is carried out the principal component analytical method dimensionality reduction in the step 5 pair step 4.Because after the EEG signals feature selection carries out the electrode reorganization, all can produce the EEG signals of higher-dimension, thereby influence the classification effectiveness and the result of EEG signals, so the dimensionality reduction process of EEG signals seems particularly important.This patent drops to 400 dimensions to the EEG signals characteristic.
EEG signals behind the step 6 pair dimensionality reduction use the linear discriminant function grader in the EEG signals expression classification enforcement module to classify afterwards, because the process of EEG signals reorganization is selected exhaustive mode for use in the step 4, experience can be known; Exhaustive method has brought computer memory big; The problem that complexity is high, therefore, here we are for addressing this problem the introducing parallel computation; Walk abreast and be primarily aimed at categorizing process, thereby improved classification time and speed greatly through concurrent operation.Like Fig. 4, the idiographic flow of detailed step display 2-6.
The step of the present invention when the test Expression Recognition is following:
Through the eeg signal acquisition module; Gather the EEG signals that tried to be measured (method is consistent with above-mentioned corresponding step); EEG signals are sent into the EEG signals pre-processing module, behind the removal noise, calculate to generate according to the EEG feature extraction module again and tried characteristic of correspondence vectorial (method is consistent with above-mentioned corresponding step); Then this characteristic vector is sent into the EEG signals expression classification and implement module, generate the classification results that stimulates based on expression at last.The result shows that the highest discrimination has surpassed 90%, and average recognition rate can realize the EEG's Recognition that expression is stimulated about 85%.
Claims (5)
1. the eeg signal classification system based on effective time sequence and electrode reorganization comprises: eeg signal acquisition module, EEG signals pre-processing module, EEG signals feature selection module, EEG signals expression classification enforcement module; It is characterized in that: described physiology EEG signals information acquisition module stimulates the original EEG signals of collection down in glad, neutral and sad different expressions, and the brain electric information that collects is passed to the EEG signals pre-processing module;
The original EEG signals that described EEG signals pre-processing module will collect carry out denoising, convert purified EEG signals to overall field intensity afterwards and send into feature selection module;
Described EEG signals feature selection module; Confirm the effective time zone of EEG signals through the peak value characteristic of overall field intensity EEG signals; EEG signals on the effective time zone are being carried out the electrode reorganization; EEG signals after the reorganization are regarded as the characteristic of EEG signals Expression Recognition, and characteristic is sent to the EEG signals expression classification implements module;
Described EEG signals expression classification is implemented module and is carried out eeg signal classification with classical sorting algorithm.
2. the eeg signal classification system based on effective time sequence and electrode reorganization according to claim 1, it is characterized in that: described classical sorting algorithm adopts the Fisher grader.
3. eeg signal classification method based on the reorganization of effective time sequence and electrode is characterized in that: may further comprise the steps:
Step 1, the experimenter is with the polar cap that powers on, and original EEG signals are to gather through amplifier, and choose all electrode positions, gather experimenter's EEG signals of different expression stimulating courses;
Step 2; The EEG signals that collect are carried out input feature vector extraction module system after the pre-processing module; Pre-processing module comprises that the EEG signals to collecting carry out denoising, and it is strong to obtain the whole audience of EEG signals, and the whole audience of EEG signals is the superposed average value of all electrode signals by force;
Step 3, the analysis strong to the EEG signals whole audience according to characteristic extracting module confirms that the effective time of EEG signals feature selection is regional;
Step 4 is carried out the electrode reorganization according to characteristic extracting module to the EEG signals of feature selection;
Step 5 is carried out dimensionality reduction to the EEG signals after the resulting reorganization of step 4 through principal component analytical method;
Step 6 is used the EEG signals expression classification to implement module neutral line discriminant function grader to the EEG signals after the characteristic extracting module extraction and is carried out classification learning and test;
During the test Expression Recognition; Through the eeg signal acquisition module, gather the EEG signals that tried to be measured, EEG signals are sent into the EEG signals pre-processing module; After removing noise; Calculate to generate according to the EEG feature extraction module again and tried the characteristic of correspondence vector, then this characteristic vector is sent into the EEG signals expression classification and implement module, obtain classification results at last.
4. the eeg signal classification method based on the reorganization of effective time sequence and electrode according to claim 3, described amplifier are 66 to lead the EEG amplifier of 10/20 method that international electroencephalography can demarcate.
5. the eeg signal classification method based on effective time sequence and electrode reorganization according to claim 3 is characterized in that:
The selection in the effective time zone of said EEG signals, regional according to the effective time that the peak value and the high-energy value of overall field intensity are confirmed;
The process of said electrode reorganization is on the basis in effective time zone, the process that different electrodes are reconfigured;
The process of said EEG signals expression classification is on parallel basis, and the EEG signals characteristic of selecting is classified.
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